Ensemble classifier for protein fold pattern recognition

By Hong-Bin Shen, Kuo-Chen Chou. 2006

364 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Methods of integrating data to uncover genotype–phenotype interactions (2015, methodology): “In addition, ensemble classifiers — such as predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability and pseudo-amino acid composition — have been used to predict protein fold recognition 65 .”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “Numerous computational methods have been proposed for sequence-based prediction of the family and superfamily (Enright et al. 2002; Melvin et al. 2007; Gewehr et al. 2007), the fold type (Ding and Dubchak 2001; Hvidsten et al. 2003; Shen and Chou 2006; Jeong et al. 2006; Taguchi and Gromiha 2007; ”
MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. (2007, methodology): “, [26–37]).”
Some remarks on protein attribute prediction and pseudo amino acid composition (2010, background): “…2009), predicting protein quaternary structural attributes (Chou and Cai, 2003a; Shen and Chou, 2009b; Xiao et al., 2009a; Xiao et al., 2010a; Zhang et al., 2008b; Zhang et al., 2006), fold pattern prediction (Shen and Chou, 2006; Shen and Chou, 2009a), and others (e.g., Georgiou et al., 2009).”
An ensemble approach to protein fold classification by integration of template‐based assignment and support vector machine classifier (2016, methodology): “…the accuracy was gradually improved from 0.56 to >0.7 (Chen and Kurgan, 2007; Damoulas and Girolami, 2008; Dong et al., 2009; Guo and Gao, 2008; Lyons et al., 2015; Rangwala and Karypis, 2005; Shamim et al., 2007; Sharma et al., 2013; Shen and Chou, 2006; Shen and Chou, 2009; Yang and Chen, 2011).”
REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, background): “PFP-Pred [232] is one of these kinds of predictors.”
A Review of Ensemble Methods in Bioinformatics (2010, background): “In [77], Shen and Chou designed nine sets of features for ensemble recognition of protein folding.”
The lncLocator: a subcellular localization predictor for long non‐coding RNAs based on a stacked ensemble classifier (2018, positive): “Our experimental results show that the final ensemble predictor is superior to any single classifier due to the reason that fusing the classifiers’ diversities is helpful to enhance the prediction performance (Shen and Chou, 2006).”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, neutral): “Furthermore, the consensus-based approaches were shown to improve predictive quality in related areas, including prediction of the protein fold types [50,51], quaternary structure type [52], transmembrane helices [37] and disorder [53–55], to name a few.”
Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique (2015, methodology): “Those taxonomic methods built on the ensemble classifiers are reported to stably improve the predictive effectiveness as compared to those based on single classifiers [1], [9], [15], [26], [37]–[39].”